Improved Robust Linearized Full-Reference Combined Metric for Remote Sensing Imaging

Remote sensing images are subject to complex noise and distortions, which reduce their quality and can lead to loss of information. Visual quality metrics can be applied to automate and optimize remote sensing image processing. The problem of taking into account distortions typical for remote sensing data in existing test image databases is considered in this paper. The efficiency of 50 full-reference quality metrics for typical remote sensing distortions is estimated. The paper proposes a robust combined metric based on alpha-trimmed mean. The influence of linearization and the use of various correlation coefficients on the formation of a robust metric are studied. The effectiveness of the solution is tested using the cross-database approach based on the test image databases including TID2013, KADID10k, MDID and LIVE Multiply Distorted Image Quality Database.